INTRODUCTION: This study aimed to elucidate the cognitive profile of patients with mild cognitive impairment with Lewy bodies (MCI-LB) and to compare it to that of patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD). METHODS: Subjects older than 60 years with probable MCI-LB (n = 60) or MCI-AD (n = 60) were recruited. All patients were tested with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) to assess their global cognitive profile. RESULTS: The MCI-AD and MCI-LB patients did not differ in total MMSE and MoCA scores. However, some sub-items in MMSE and MoCA were shown to be screening markers for differentiating MCI-LB from MCI-AD. In the visuoconstructive test, the total score and hands subitem score in the clock-drawing test were significantly lower in MCI-LB than in MCI-AD. As for the executive function, the 'animal fluency test', 'repeat digits backward test' and 'take paper by your right hand' in MMSE all showed lower scores in MCI-LB compared with MCI-AD. As for memory, 'velvet' and 'church' in MoCA and 'ball' and 'national flag' in MMSE had lower scores in MCI-AD than in MCI-LB. CONCLUSION This study presents the cognitive profile of patients with MCI-LB. In line with the literature on Dementia with Lewy bodies, our results showed lower performance on tests for visuoconstructive and executive function, whereas memory remained relatively spared in the early period.
Humans ; Cognitive Dysfunction ; Alzheimer Disease/diagnosis* ; Neuropsychological Tests ; Cognition

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neural Networks, Computer ; Magnetic Resonance Imaging/methods* ; Neuroimaging/methods* ; Diagnosis, Computer-Assisted ; Brain ; Cognitive Dysfunction

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Humans ; Mental Disorders/diagnosis* ; Alzheimer Disease/diagnosis* ; Brain Injuries ; Electroencephalography ; Recognition, Psychology

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.
Humans ; Alzheimer Disease/diagnosis* ; Neurodegenerative Diseases ; Early Diagnosis ; Cognitive Dysfunction ; Biomarkers

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.
Humans ; Alzheimer Disease/diagnosis* ; Neurodegenerative Diseases ; Early Diagnosis ; Cognitive Dysfunction ; Biomarkers

Alzheimer's disease (AD) is an age-related neurodegenerative disorder. It is expected that the incidence of AD will increase exponentially in the coming decades. The clinical and research application of AD biomarkers has gone through a long process. At present, the clinical diagnostic criteria for AD mainly include the IWG-2 criteria developed by International Working Group (IWG), the NIA-AA criteria formulated by the National Institute on Aging and Alzheimer's Association (NIA-AA) and the "Guidelines for the Diagnosis and Treatment of Alzheimer's Disease in China (2020 version)" released by the Professional Committee on Alzheimer's Disease and Related Diseases of the Chinese Geriatric Health Care Association (Alzheimer's Disease Chinese, ADC). Cerebrospinal fluid biomarkers such as Aβ₄₂, T-tau and P-tau are recognized as central biomarkers for AD, besides, the development of new molecules in other pathophysiological pathway that can be used as biomarkers for the diagnosis of AD have made great progress in the last decade. This article elaborates studies of the application guidelines of AD biomarkers and highlights the research progress of biomarkers in AD pathophysiological pathway.
Aged ; Alzheimer Disease/diagnosis* ; Biomarkers ; China ; Humans ; National Institute on Aging (U.S.) ; United States

The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer's diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer's disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnosis* ; Neural Networks, Computer ; Machine Learning ; Brain ; Electroencephalography

Alzheimer Disease/diagnosis* ; Cognitive Dysfunction ; Disease Progression ; Humans ; Neuroimaging

Alzheimer Disease/diagnosis* ; Biomarkers ; Electroencephalography ; Humans ; Sleep